Knowledge is defined as the information about a domain that can be queried and used to solve problems in that domain. Knowledge can be represented by a representation scheme to form an Artificial Intelligence (AI) system. In other words, the representation scheme may be understood as the manner in which knowledge is handled to solve problems in a particular domain. Several organizations are involved in modeling knowledge representation techniques in Artificial Intelligence (AI) for several technology domains. For example, knowledge representation may be used in healthcare management, hospitality, transport, integrated circuit design, computer architecture design, social network systems, and the like. Such knowledge representation (KR) techniques can be considered as core component of knowledge management for an enterprise.
Nowadays, some of these knowledge representation techniques are modeled using graph databases due to their capability of storing an enormous volume of large, dynamic, and sparse datasets. Generally, the graph databases are modeled using graph structures to represent and store datasets. The graph structures conveniently encode diverse relations for information retrieval, by virtue of its inherent connectedness. The graph structures include nodes and edges. The nodes represent entities and the edges represent relationships among the entities. Further, the graph structures can be encoded, traversed, partitioned, colored, and clustered, based on the real-world use case scenarios and solutions for knowledge representation and knowledge management.
Each node and edge can have several properties associated therewith for storing attributes of the node and edge. These properties can further be attached to each node and edge as key-value pairs. These properties facilitate in information retrieval from the graph database by means of indexed search based on these stored properties. Further, information retrieval can also be performed by means of graph traversal.